Readiness of Quantum Optimization Machines for Industrial Applications
Readiness of Quantum Optimization Machines for Industrial Applications
复制标题
工业应用量子优化机器的准备情况
DOI：
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发表时间：
2017
影响因子：
4.6
通讯作者：
R. Biswas
中科院分区：
文献类型：
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作者：
A. Perdomo;A. Feldman;A. Ozaeta;S. Isakov;Zheng Zhu;B. O’Gorman;H. Katzgraber;Alexander Diedrich;H. Neven;Johan de Kleer;Brad Lackey;R. Biswas
人们已经多次尝试证明，量子退火机上的量子退火机，尤其是量子退火机上的量子退火机，有可能超越目前在CMOS技术上实现的经典优化算法。这些设备的基准一直备受争议。最初，随机的自旋玻璃问题被使用，然而，这些问题很快被证明不太适合检测任何量子加速。随后，基准测试转向精心设计的合成问题，旨在突出硬件的量子性质，同时(通常)确保经典优化技术在这些问题上表现不佳。更糟糕的是，与经典的优化技术相比，到目前为止，随着问题变量数量的增加，改进规模的真正迹象仍然难以捉摸。在这里，我们分析了量子退火机对实际应用问题的准备情况。这些通常不是随机的，并且具有在合成基准中很难捕获的底层结构，因此给优化技术带来了意想不到的挑战，无论是经典的还是量子的。考虑到D波量子退火炉以外的结构，我们提出了数字电路故障诊断的综合计算标度分析。我们发现，乘法器电路中由真实数据生成的实例比其他具有可比变量数量的代表性随机自旋玻璃基准测试更难。尽管我们的结果表明，横场量子退火法的性能优于最先进的经典优化算法，但这些基准实例在输入大小上是硬的，因此是第一个工业应用，非常适合测试近期量子退火机和其他优化问题的量子算法策略。
There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current classical optimization algorithms implemented on CMOS technologies. The benchmarking of these devices has been controversial. Initially, random spin-glass problems were used, however, these were quickly shown to be not well suited to detect any quantum speedup. Subsequently, benchmarking shifted to carefully crafted synthetic problems designed to highlight the quantum nature of the hardware while (often) ensuring that classical optimization techniques do not perform well on them. Even worse, to date a true sign of improved scaling with the number of problem variables remains elusive when compared to classical optimization techniques. Here, we analyze the readiness of quantum annealing machines for real-world application problems. These are typically not random and have an underlying structure that is hard to capture in synthetic benchmarks, thus posing unexpected challenges for optimization techniques, both classical and quantum alike. We present a comprehensive computational scaling analysis of fault diagnosis in digital circuits, considering architectures beyond D-wave quantum annealers. We find that the instances generated from real data in multiplier circuits are harder than other representative random spin-glass benchmarks with a comparable number of variables. Although our results show that transverse-field quantum annealing is outperformed by state-of-the-art classical optimization algorithms, these benchmark instances are hard and small in the size of the input, therefore representing the first industrial application ideally suited for testing near-term quantum annealers and other quantum algorithmic strategies for optimization problems.
影响因子：
7.6
作者：
Chancellor, N.;Zohren, S.;Warburton, P. A.
通讯作者：
Warburton, P. A.